Learning Clean Generative Processes from Noisy Measurements
This paper introduces a method to train generative models of clean images using only noisy observations, without ever seeing clean data. It shows that Noise2Noise regression can learn posterior predictions at or above the training noise level, and that imposing a consistency condition uniquely determines the continuation to the clean image distribution for several noise types. Experiments on CIFAR-10, photon-counting microscopy, and synthetic aperture radar demonstrate that the consistency relation repairs the generation process below the noise level, which otherwise deteriorates.
Why it matters: This is a novel theoretical and practical contribution that enables training generative models on noisy data alone, with broad applicability in imaging domains where clean data is unavailable.